to the steepest but taking account of previous directions). For example,
Figure 16.3 shows a function in 2D, where lightness represents the
function value.
This has a single minimum at the origin, shown at the centre of the
diagram. A descent algorithm started at any point would just walk
towards the minimum, adjusting its step size as it converged.
But Figure 16.4 shows a different kind of 2D function.
This time there are many minima and descent is practically use-
less. The problem is like trying to nd the highest tuft in the pile of
a carpet.
So in multidimensional optimization there are two extremes, and
some interesting places between. At one extreme are the functions
with one minimum. The minimum can be found quickly, by descent.
At the other extreme are noiselike functions, where one of the huge
number of local minima happens to be the global minimum. The only
hope of nding the global minimum is exhaustive search. The interest-
ing cases in between are where there are a signicant number of local
minima that are good enough they may not be the global minimum,
but they are close in value. Finding one of these will take a lot longer
than a single descent, but much shorter than searching every possible
value. Methods for doing just this include simulated annealing and
various kinds of evolutionary algorithms. There is another interesting
case though, exemplied by the 2D function in Figure 16.5.
This is the sum of a simple, single minimum, function and noise. If
we were to take a cross-section through it, we would get a 1D equiva-
lent something like Figure 16.6.
This is another intermediate case between global and local opti-
mization. But we dont want to use as expensive a method as simu-
lated annealing here. We really want a kind of noise-resilient local
optimizer. That is the problem addressed in this chapter.
Multidimensional optimization is a standard numerical method, so the
place to start is the contents and index pages of standard textbooks.
Well, not quite the rst. A Google search takes only a few seconds, and
332 Software Design for Engineers and Scientists
Figure 16.3 A two-dimensional function
with a single minimum at the origin
Figure 16.4 A two-dimensional function
with many minima
Figure 16.5 A noisy two-dimensional
function
16.3 Researching possible
solutions
y
x
Figure 16.6 A noisy one-dimensional
function

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